The Experts below are selected from a list of 453834 Experts worldwide ranked by ideXlab platform
Alex C Kot - One of the best experts on this subject based on the ideXlab platform.
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skeleton based human action recognition with Global Context aware attention lstm networks
IEEE Transactions on Image Processing, 2018Co-Authors: Jun Liu, Gang Wang, Lingyu Duan, Kamila Abdiyeva, Alex C KotAbstract:Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, long short-term memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, Global Context-aware attention LSTM, for skeleton-based action recognition, which is capable of selectively focusing on the informative joints in each frame by using a Global Context memory cell. To further improve the attention capability, we also introduce a recurrent attention mechanism, with which the attention performance of our network can be enhanced progressively. Besides, a two-stream framework, which leverages coarse-grained attention and fine-grained attention, is also introduced. The proposed method achieves state-of-the-art performance on five challenging datasets for skeleton-based action recognition.
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skeleton based human action recognition with Global Context aware attention lstm networks
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Jun Liu, Gang Wang, Lingyu Duan, Kamila Abdiyeva, Alex C KotAbstract:Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, Long Short-Term Memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action recognition. This network is capable of selectively focusing on the informative joints in each frame of each skeleton sequence by using a Global Context memory cell. To further improve the attention capability of our network, we also introduce a recurrent attention mechanism, with which the attention performance of the network can be enhanced progressively. Moreover, we propose a stepwise training scheme in order to train our network effectively. Our approach achieves state-of-the-art performance on five challenging benchmark datasets for skeleton based action recognition.
Junping Zhao - One of the best experts on this subject based on the ideXlab platform.
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GC-Net: Global Context network for medical image segmentation
Computer methods and programs in biomedicine, 2019Co-Authors: Jing Tong, Zhengming Chen, Junping ZhaoAbstract:Abstract Background and Objective Medical image segmentation plays an important role in many clinical applications such as disease diagnosis, surgery planning, and computer-assisted therapy. However, it is a very challenging task due to variant images qualities, complex shapes of objects, and the existence of outliers. Recently, researchers have presented deep learning methods to segment medical images. However, these methods often use the high-level features of the convolutional neural network directly or the high-level features combined with the shallow features, thus ignoring the role of the Global Context features for the segmentation task. Consequently, they have limited capability on extensive medical segmentation tasks. The purpose of this work is to devise a neural network with Global Context feature information for accomplishing medical image segmentation of different tasks. Methods The proposed Global Context network (GC-Net) consists of two components; feature encoding and decoding modules. We use multiple convolutions and batch normalization layers in the encoding module. On the other hand, the decoding module is formed by a proposed Global Context attention (GCA) block and squeeze and excitation pyramid pooling (SEPP) block. The GCA module connects low-level and high-level features to produce more representative features, while the SEPP module increases the size of the receptive field and the ability of multi-scale feature fusion. Moreover, a weighted cross entropy loss is designed to better balance the segmented and non-segmented regions. Results The proposed GC-Net is validated on three publicly available datasets and one local dataset. The tested medical segmentation tasks include segmentation of intracranial blood vessel, retinal vessels, cell contours, and lung. Experiments demonstrate that, our network outperforms state-of-the-art methods concerning several commonly used evaluation metrics. Conclusion Medical segmentation of different tasks can be accurately and effectively achieved by devising a deep convolutional neural network with a Global Context attention mechanism.
Jun Liu - One of the best experts on this subject based on the ideXlab platform.
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skeleton based human action recognition with Global Context aware attention lstm networks
IEEE Transactions on Image Processing, 2018Co-Authors: Jun Liu, Gang Wang, Lingyu Duan, Kamila Abdiyeva, Alex C KotAbstract:Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, long short-term memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, Global Context-aware attention LSTM, for skeleton-based action recognition, which is capable of selectively focusing on the informative joints in each frame by using a Global Context memory cell. To further improve the attention capability, we also introduce a recurrent attention mechanism, with which the attention performance of our network can be enhanced progressively. Besides, a two-stream framework, which leverages coarse-grained attention and fine-grained attention, is also introduced. The proposed method achieves state-of-the-art performance on five challenging datasets for skeleton-based action recognition.
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skeleton based human action recognition with Global Context aware attention lstm networks
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Jun Liu, Gang Wang, Lingyu Duan, Kamila Abdiyeva, Alex C KotAbstract:Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, Long Short-Term Memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action recognition. This network is capable of selectively focusing on the informative joints in each frame of each skeleton sequence by using a Global Context memory cell. To further improve the attention capability of our network, we also introduce a recurrent attention mechanism, with which the attention performance of the network can be enhanced progressively. Moreover, we propose a stepwise training scheme in order to train our network effectively. Our approach achieves state-of-the-art performance on five challenging benchmark datasets for skeleton based action recognition.
Ming-hsuan Yang - One of the best experts on this subject based on the ideXlab platform.
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Scene Parsing with Global Context Embedding
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Wei-chih Hung, Yi-hsuan Tsai, Xiaohui Shen, Zhe Lin, Kalyan Sunkavalli, Ming-hsuan YangAbstract:We present a scene parsing method that utilizes Global Context information based on both the parametric and non- parametric models. Compared to previous methods that only exploit the local relationship between objects, we train a Context network based on scene similarities to generate feature representations for Global Contexts. In addition, these learned features are utilized to generate Global and spatial priors for explicit classes inference. We then design modules to embed the feature representations and the priors into the segmentation network as additional Global Context cues. We show that the proposed method can eliminate false positives that are not compatible with the Global Context representations. Experiments on both the MIT ADE20K and PASCAL Context datasets show that the proposed method performs favorably against existing methods.
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ICCV - Scene Parsing with Global Context Embedding
2017 IEEE International Conference on Computer Vision (ICCV), 2017Co-Authors: Wei-chih Hung, Yi-hsuan Tsai, Xiaohui Shen, Zhe Lin, Kalyan Sunkavalli, Ming-hsuan YangAbstract:We present a scene parsing method that utilizes Global Context information based on both the parametric and nonparametric models. Compared to previous methods that only exploit the local relationship between objects, we train a Context network based on scene similarities to generate feature representations for Global Contexts. In addition, these learned features are utilized to generate Global and spatial priors for explicit classes inference. We then design modules to embed the feature representations and the priors into the segmentation network as additional Global Context cues. We show that the proposed method can eliminate false positives that are not compatible with the Global Context representations. Experiments on both the MIT ADE20K and PASCAL Context datasets show that the proposed method performs favorably against existing methods.
Linda G Shapiro - One of the best experts on this subject based on the ideXlab platform.
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a sift descriptor with Global Context
Computer Vision and Pattern Recognition, 2005Co-Authors: Eric N Mortensen, Hongli Deng, Linda G ShapiroAbstract:Matching points between multiple images of a scene is a vital component of many computer vision tasks. Point matching involves creating a succinct and discriminative descriptor for each point. While current descriptors such as SIFT can find matches between features with unique local neighborhoods, these descriptors typically fail to consider Global Context to resolve ambiguities that can occur locally when an image has multiple similar regions. This paper presents a feature descriptor that augments SIFT with a Global Context vector that adds curvilinear shape information from a much larger neighborhood, thus reducing mismatches when multiple local descriptors are similar. It also provides a more robust method for handling 2D nonrigid transformations since points are more effectively matched individually at a Global scale rather than constraining multiple matched points to be mapped via a planar homography. We have tested our technique on various images and compare matching accuracy between the SIFT descriptor with Global Context to that without.
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CVPR (1) - A SIFT descriptor with Global Context
2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 1Co-Authors: Eric N Mortensen, Hongli Deng, Linda G ShapiroAbstract:Matching points between multiple images of a scene is a vital component of many computer vision tasks. Point matching involves creating a succinct and discriminative descriptor for each point. While current descriptors such as SIFT can find matches between features with unique local neighborhoods, these descriptors typically fail to consider Global Context to resolve ambiguities that can occur locally when an image has multiple similar regions. This paper presents a feature descriptor that augments SIFT with a Global Context vector that adds curvilinear shape information from a much larger neighborhood, thus reducing mismatches when multiple local descriptors are similar. It also provides a more robust method for handling 2D nonrigid transformations since points are more effectively matched individually at a Global scale rather than constraining multiple matched points to be mapped via a planar homography. We have tested our technique on various images and compare matching accuracy between the SIFT descriptor with Global Context to that without.
